Conditional denoising diffusion-based channel estimation for fast time-varying MIMO-OFDM systems

We propose an innovative conditional denoising diffusion-based channel estimation (CDDCE) scheme for fast time-varying multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. This intelligent CDDCE model delicately adapts the denoising diffusion probabilistic model (DDPM) to conditional channel state information (CSI) generation and performs efficient channel estimation with a stochastic iterative denoising process. Specifically, the CDDCE model utilizes a Markov chain that gradually adds Gaussian noise to the customized preprocessed genuine CSI according to the cosine variance schedule for the forward Gaussian diffusion process . Then, the channel estimation begins with pure Gaussian noise and repeatedly refines the conditional roughly estimated CSI by a specialized U-Net trained on denoising at different noise levels for the reverse iterative refinement process. Numerical results show that our CDDCE scheme significantly outperforms classical approaches and three cutting-edge deep learning (DL)-based ones, indicating its eminent capability to learn the statistical characteristics of wireless channels. Besides, we demonstrate that the CDDCE scheme exhibits excellent robustness against various channel distortions and interference: when (i) there are a restricted number of pilot symbols, (ii) the cyclic prefix (CP) is omitted, (iii) the clipping noise is introduced, and (iv) the offline and online channel conditions are mismatched.